2021-02-21 22:57:50 +01:00

61 lines
1.6 KiB
Python

# -*- coding: utf-8 -*-
"""
This python module implements visualization techniques/modes
Copyright (C) 2018 SINTEF ICT
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
import numpy as np
from matplotlib.colors import Normalize
def genSchlieren(rho):
#Compute length of z-component of normalized gradient vector
normal = np.gradient(rho) #[x, y, 1]
length = 1.0 / np.sqrt(normal[0]**2 + normal[1]**2 + 1.0)
schlieren = np.power(length, 128)
return schlieren
def genVorticity(rho, rho_u, rho_v):
u = rho_u / rho
v = rho_v / rho
u = np.sqrt(u**2 + v**2)
u_max = u.max()
du_dy, _ = np.gradient(u)
_, dv_dx = np.gradient(v)
#Length of curl
curl = dv_dx - du_dy
return curl
def genColors(rho, rho_u, rho_v, cmap, vmax, vmin):
schlieren = genSchlieren(rho)
curl = genVorticity(rho, rho_u, rho_v)
colors = Normalize(vmin, vmax, clip=True)(curl)
colors = cmap(colors)
for k in range(3):
colors[:,:,k] = colors[:,:,k]*schlieren
return colors